In deep learning, the implementation of algorithms and their debugging takes an important part in the research time. Therefore, numerous types of libraries have been developed to help researchers to quickly implements their algorithms. Those libraries might be very low level for optimized use of hardware such as Cudnn [1]} or Cuda [2]} or it might also help to optimized mathematical operation such as NumPy [3]}. There are also higher-level libraries that gathered together a set of tools for research. In machine and deep learning, multiple libraries such as scikit-learn [4]}, Pytorch [5]} or TensorFlow [6]} ease the conception of learning algorithms.
